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A real product solves a specific problem in a repeatable, trustworthy, and economically viable way.
And that means understanding two very different costs:
1. Cost to build –> fast, cheap, often exciting.
2. Cost to own –> ongoing, invisible, often underestimated.
When it comes to AI, think cost related to compliance, model drift, governance, user support, retraining, changing data pipelines, and more and much more…
It’s not just technical maintenance, it’s product ownership at scale.
And in many cases, it’s where even great ideas quietly fail.
Chatbots that spiral into legal nightmares.
“Quick tools” that become forever projects.
Forecasting models that break under real-world pressure.
The illusion of AI quick wins is real.
And unless product teams account for the full cost of ownership, they’re not building products, they’re building liabilities.
So next time you see a cool AI demo, ask:
– Who’s really going to use this?
– Will users trust it enough to adopt it?
– What will it take to keep it relevant?
– And ultimately… will it generate more value than it consumes?
Because nowadays, when it comes to AI, building is easy, Owning is not!!
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